A decision tree is a good classifier with a transparent decision mechanism. Decision-tree building methods usually have problems in splitting the learning samples into more subsets, because of the nature of the tree. If the classification...
moreA decision tree is a good classifier with a transparent decision mechanism. Decision-tree building methods usually have problems in splitting the learning samples into more subsets, because of the nature of the tree. If the classification into such subsets is not possible, it is better to put the classification decision on to some other classifier. This leads to the introduction of a null classification, which simply means that no classification is possible in this step. This approach is sensible with evolutionary methods, as they can handle a number of trees simultaneously. In the process of construction, we have to address the problem of whether a classification is sensible. The performance of the proposed model has been tested on several data sets and the results presented on one such data set show its potential
... Page 5. 274 Matej Łprogar et al. ... Morgan Kaufmann Publishers Inc (1998) 3. Boudoulas, H., Kolibash, AJ, Baker, P., King, BD, Wooley, CF: Mitral Valve Prolapse and the Mitral Valve Prolapse Syndrome: A Diagnostic Classification and...
more... Page 5. 274 Matej Łprogar et al. ... Morgan Kaufmann Publishers Inc (1998) 3. Boudoulas, H., Kolibash, AJ, Baker, P., King, BD, Wooley, CF: Mitral Valve Prolapse and the Mitral Valve Prolapse Syndrome: A Diagnostic Classification and Pathogenesis of Symptoms. ...
The paper shows the importance of e-health applications for electronic healthcare development. It describes several e-health applications for health data collecting and sharing that are running in the Czech Republic. These are IZIP...
moreThe paper shows the importance of e-health applications for electronic healthcare development. It describes several e-health applications for health data collecting and sharing that are running in the Czech Republic. These are IZIP system, electronic health record MUDR and K4CARE project applications. The e3-health concept is considered as a tool for judging e-health applications in different healthcare settings.
A decision tree is a good classifier with a transparent decision mechanism. Decision-tree building methods usually have problems in splitting the learning samples into more subsets, because of the nature of the tree. If the classification...
moreA decision tree is a good classifier with a transparent decision mechanism. Decision-tree building methods usually have problems in splitting the learning samples into more subsets, because of the nature of the tree. If the classification into such subsets is not possible, it is better to put the classification decision on to some other classifier. This leads to the introduction of a null classification, which simply means that no classification is possible in this step. This approach is sensible with evolutionary methods, as they can handle a number of trees simultaneously. In the process of construction, we have to address the problem of whether a classification is sensible. The performance of the proposed model has been tested on several data sets and the results presented on one such data set show its potential
... Page 5. 274 Matej Łprogar et al. ... Morgan Kaufmann Publishers Inc (1998) 3. Boudoulas, H., Kolibash, AJ, Baker, P., King, BD, Wooley, CF: Mitral Valve Prolapse and the Mitral Valve Prolapse Syndrome: A Diagnostic Classification and...
more... Page 5. 274 Matej Łprogar et al. ... Morgan Kaufmann Publishers Inc (1998) 3. Boudoulas, H., Kolibash, AJ, Baker, P., King, BD, Wooley, CF: Mitral Valve Prolapse and the Mitral Valve Prolapse Syndrome: A Diagnostic Classification and Pathogenesis of Symptoms. ...
... Jiri Klema* Laboratory for System Design, Faculty of Electrical Engineering and Computer Science, Smetanova ulica 17, 2000 Maribor, Slovenia Phone: +386-62-2207455, Fax: +386-62-211178 e-mail:{matej.sprogar, kokol, milan ... [3]...
more... Jiri Klema* Laboratory for System Design, Faculty of Electrical Engineering and Computer Science, Smetanova ulica 17, 2000 Maribor, Slovenia Phone: +386-62-2207455, Fax: +386-62-211178 e-mail:{matej.sprogar, kokol, milan ... [3] Boudoulas, H., Kolibash, AJ, Baker, P., King ...
Interpretation of cardiotocogram (CTG) is a difficult task since its evaluation is complicated by a great inter- and intra-individual variability. Previous studies have predominantly analyzed...
moreInterpretation of cardiotocogram (CTG) is a difficult task since its evaluation is complicated by a great inter- and intra-individual variability. Previous studies have predominantly analyzed clinicians' agreement on CTG evaluation based on quantitative measures (e.g. kappa coefficient) that do not offer any insight into clinical decision making. In this paper we aim to examine the agreement on evaluation in detail and provide data-driven analysis of clinical evaluation. For this study, nine obstetricians provided clinical evaluation of 634 CTG recordings (each ca. 60min long). We studied the agreement on evaluation and its dependence on the increasing number of clinicians involved in the final decision. We showed that despite of large number of clinicians the agreement on CTG evaluations is difficult to reach. The main reason is inherent inter- and intra-observer variability of CTG evaluation. Latent class model provides better and more natural way to aggregate the CTG evaluation than the majority voting especially for larger number of clinicians. Significant improvement was reached in particular for the pathological evaluation - giving a new insight into the process of CTG evaluation. Further, the analysis of latent class model revealed that clinicians unconsciously use four classes when evaluating CTG recordings, despite the fact that the clinical evaluation was based on FIGO guidelines where three classes are defined.
ABSTRACT This paper details the process of mining information from a hospital information system that has been designed approximately 15 years ago. The information is distributed within database tables in large textual attributes with a...
moreABSTRACT This paper details the process of mining information from a hospital information system that has been designed approximately 15 years ago. The information is distributed within database tables in large textual attributes with a free structure. Information retrieval from these information is necessary for complementing cardiotocography signals with additional information that is to be implemented in a decision support system. The basic statistical overview (n-gram analysis) helped with the insight into data structure, however more sophisticated methods have to be used as human (and expert) processing of the whole data were out of consideration: over 620,000 text fields contained text reports in natural language with (many) typographical errors, duplicates, ambiguities, syntax errors and many (nonstandard) abbreviations. There was a strong need to efficiently determine the overall structure of the database and discover information that is important from the clinical point of view. We have used three different methods: k-means, self-organizing map and a self-organizing approach inspired by ant-colonies that performed clustering of the records. The records were visualized and revealed the most prominent information structure(s) that were consulted with medical experts and served for further mining from the database. The outcome of this task is a set of ordered or nominal attributes with a structural information that is available for rule discovery mining and automated processing for the research of asphyxia prediction during delivery. The proposed methodology has significantly reduced the processing time of loosely structured textual records for both IT and medical experts.
Andreas ZEKL (Stuttgart, DE) Anthony E. WARD (York, UK) Antoanela NAAJI (Arad, RO) Almudena SUAREZ (Santander, ES) Andrzej WAC-WLODARCZYK (Lublin, PL) Anna FRIESEL (Copenhagen, DK) Blaise CONRARD (Villeneuve d'Ascq, FR) Bostjan...
moreAndreas ZEKL (Stuttgart, DE) Anthony E. WARD (York, UK) Antoanela NAAJI (Arad, RO) Almudena SUAREZ (Santander, ES) Andrzej WAC-WLODARCZYK (Lublin, PL) Anna FRIESEL (Copenhagen, DK) Blaise CONRARD (Villeneuve d'Ascq, FR) Bostjan BRUMEN (Maribor, SI) Carlos Machado FERREIRA (Coimbra, PT) Cyril BURKLEY (Limerick, IE) Dante del CORSO (Torino, IT) Dimiter DIMITROV (Sofia, BG) Dorin POPESCU (Craiova, RO) Everardo REYES (Monterrey, MX) Fernando MACIEL BARBOSA (Porto, PT) Georgios PAPADOURAKIS ( ...
Abstract In many industry and research areas, data mining is a crucial process. This paper presents an evolving structure of classifiers (random forest) where the trees are generated by hybrid method combining ant colony metaheuristics...
moreAbstract In many industry and research areas, data mining is a crucial process. This paper presents an evolving structure of classifiers (random forest) where the trees are generated by hybrid method combining ant colony metaheuristics and evolutionary computing ...
The traditional way of electroencephalographic data analysis is visual inspection. Expert's classification may not always correspond completely with measured data due to the subjective evaluation and the fact that this kind of...
moreThe traditional way of electroencephalographic data analysis is visual inspection. Expert's classification may not always correspond completely with measured data due to the subjective evaluation and the fact that this kind of evaluation is tedious and time consuming. This paper presents a decision support tool developed both for clinical and nonclinical applications in this field. The proposed solution comprises several
Polysomnographic (PSG) signal processing represents a complex process consisting of several subsequent steps, namely pre-processing, segmentation, extraction of descriptive features, and classification. In this paper we focus on...
morePolysomnographic (PSG) signal processing represents a complex process consisting of several subsequent steps, namely pre-processing, segmentation, extraction of descriptive features, and classification. In this paper we focus on visualization methods that are also unseparable part of the whole process. The aim of these methods is to ease the work of medical doctors and to show trends that are not obvious when performing a manual inspection of the recorded signal. In this study, the designed methods are applied to neonatal PSG data and enable the enhancement in visual differentiation between three important behavioral states: quiet sleep (QS), active sleep (AS) and wakefulness (WK). The ratio of these states is a significant indicator of the maturity of the newborn brain in clinical practice.
The paper is focused on description of an ongoing project of a pilot study and implementation of a multi-agent system for management of medical documentation in a hospital. First we analyzed the problem and divided it into four groups of...
moreThe paper is focused on description of an ongoing project of a pilot study and implementation of a multi-agent system for management of medical documentation in a hospital. First we analyzed the problem and divided it into four groups of tasks: storing and retrieving stored data, user interaction, data archiving, and system security. All these tasks are performed by corresponding agents, namely user interface agent, database agent, archive agent, and security agent. Communication between the agents is a crucial point of the system operation. The system has been designed as an open system and we assume that it will be extended by additional agents with new functions, e.g. decision support, biomedical signal evaluation, laboratory test evaluation.
